Best Data Science Classes Online

For students who are interested in the work that data scientists do in their careers, this seminar introduces the field and how to join it. The tutorial begins with an overview of the role that Python plays in data science as an open-source programming language. Students learn about the skills and tools that data scientists use their their day-to-day work as well as how to work with data without advanced math skills. At the end of the session, students also get to see data science in action with a demonstration.

This course guides students through a practice-based curriculum designed to help them enter careers in data analytics. Students start by learning about structured thinking for data analysis and how businesses solve economic, financial, and statistical problems with data analytic solutions. By gaining skills in SQL, Python, and Tableau, students learn to analyze and visualize data and present their findings and conclusions. Students graduate with a porfolio of work, including two capstone data analytics projects.

Through this sequence of two courses, students prepare for careers in data analytics. Students begin by learning about the theory and applications of data analytics, gaining skills in using pivot tables and statistics in Excel to understand, analyze, and visualize data. Then, students dig into practicing data analysis, developing proficiency with Python, SQL, and Tableau before learning about big data, machine learning, and ethical issues in data analysis. Students complete the course with a portfolio of professional data analytics work.

Students in this course track learn the essential skills of a data scientist. As they move through the course track, students learn about data management and statistical analysis, as well as how businesses make use of data insights. Students build visualizations in Power BI, Seaborn, and Tableau and develop skills for data research, deep learning, and machine learning. A final exam testing their new knowledge helps students earn a digital badge.

These eight sequential courses prepare beginner data analysts for careers in the field. Students start by learning about data and the kinds of problems data analysis can solve, then develop skills in SQL in order to extract, store, and clean data for analysis. Students also gain experience with data visualization and programming with R for data analysis. In the final course, students develop a data analysis case study for their capstone project and benefit from lessons in job hunting before they graduate with a certificate from Google.

Through this bootcamp, students with experience in programming and statistics can jumpstart their careers in data science. Students learn to use Python, SQL, Pandas, and APIs for data science as they develop skills for data wrangling and data analysis. In addition, students gain experience with inferential statistics, machine learning, deep learning, and natural language processing as they advance through the course and begin to scale up their work. By the end of the course, students will have produced two capstone projects.

Offered through University of California San Diego Extension, this course prepares advanced programmers for careers in machine learning. Students start with learning about and analyzing data, before moving into machine learning algorithms and deep learning. Students gain hands-on experience developing machine learning platforms and have the option of specializing in advanced deep learning, image processing, or natural language processing with additional study. Career coaching is integrated into the course curriculum, and students produce a machine learning capstone project for their professional portfolio.

Designed for beginners, this course helps students learn the programming skills to jumpstart their studies in data science. Students work in two primary programming languages, SQL and Python (with Pandas and NumPy). The course introduces students to joining, querying, and subquerying tables and working with data structures, variables, and functions. Students complete two major projects and learn to use GitHub's version control features.

This course track introduces students to the field and tools of data science. Students learn to scrape, filter, transform, explore, and integrate data before learning the fundamentals of data analysis and visualization. Then, students get an orientation to machine learning, big data, and streaming data. Students also learn to communicate their findings to an audience before earning a digital badge.

After studying this course, students with a background in Python, machine learning, SQL, and statistics will have the essential skills for doing work with data science. The course starts by teaching students data visualization and presentation before diving into data pipelines with a unit on data engineering. Students then discover best practices for designing and testing experiments. At the end of the course, students develop a capstone project to practice their new data science skills.

In this course, Python students can apply their programming skills to AI for navigating the stock market. Students learn about quantitative trading and optimizing their portfolios. Then, students discover how to use natural language processing and deep learning to study the stock market. Along the way, students complete hands-on projects, developing their algorithms for trading.

Students new to data science learn industry-standard coding languages in this course. In the first unit, students develop familiarity with SQL, including writing queries and subqueries joining tables. In the second unit, students learn to program in R and use ggplot2 for data visualizations. By the end of the course, students will have completed two hands-on projects and practiced version control with GitHub.